Apparatus and methods for probing sensor operation of continuous analyte sensing and auto-calibration

ABSTRACT

Apparatus and methods are operative to probe the condition of a sensor either initially, at any point thereafter or continuously during a continuous sensor operation for measuring an analyte in a bodily fluid (such as performed by, e.g., a continuous glucose monitoring (CGM) sensor). Results of the probe may include calibration indices determined from electrical signals obtained during the probe. The calibration indices may indicate whether in-situ adjustment of the sensor&#39;s calibration should be performed either initially and/or at random check points. Probing potential modulation parameters also may be used during analyte calculations to reduce the effects of lot-to-lot sensitivity variations, sensitivity drift during monitoring, temperature, interferents, and/or the like. Other aspects are disclosed.

The present application claims priority to U.S. Provisional PatentApplication No. 62/801,592, filed Feb. 5, 2019, and titled “APPARATUSAND METHODS FOR PROBING SENSOR OPERATION OF CONTINUOUS ANALYTE SENSINGAND AUTO-CALIBRATION” which is hereby incorporated by reference hereinin its entirety.

FIELD

This disclosure relates to continuous sensor monitoring of an analyte ina bodily fluid.

BACKGROUND

Continuous analyte sensing in an in-vivo or in-vitro sample, such as,e.g., continuous glucose monitoring (CGM), has become a routine sensingoperation in the field of medical devices, and more specifically, indiabetes care. For biosensors that measure analytes in a whole bloodsample with discrete sensing, such as, e.g., pricking a finger to obtaina blood sample, the sample's temperature and hematocrit of the bloodsample may be major sources of error. However, for sensors deployed in anon-whole blood environment with relatively constant temperatures, suchas sensors used in a continuous in-vivo sensing operation, other sensorerror sources may exist.

Accordingly, improved apparatus and methods of in-situ calibration ofCGM sensors are desired.

SUMMARY

According to aspects of the disclosure, apparatus and methods areprovided that may probe a sensor with respect to its sensitivity oroperating conditions, extract sensitivity indices for the sensor'soperating conditions, and provide an in-situ calibration other than thefactory calibration as needed.

In some embodiments, a method of compensating for errors duringcontinuous glucose monitoring (CGM) measurements is provided thatincludes providing a CGM device including a sensor, a memory and aprocessor; applying a constant voltage potential to the sensor,measuring primary current signals resulting from the constant voltagepotential and storing measured primary current signals in the memory;between measurements of primary current signals, applying a probingpotential modulation sequence to the sensor, measuring probing potentialmodulation current signals resulting from the probing potentialmodulation sequence and storing measured probing potential modulationcurrent signals in the memory; and for each primary current signal,employing the primary current signal and a plurality of the measuredprobing potential modulation current signals associated with the primarycurrent signal to determine a glucose value.

In some embodiments, a method of making a continuous glucose monitoring(CGM) device is provided that includes creating a prediction equationbased on a plurality of probing potential modulation current signalsmeasured for a reference CGM sensor in response to a probing potentialmodulation sequence applied to the reference CGM sensor before or afterprimary current signals are measured for the reference CGM sensor;providing a CGM device including a sensor, a memory and a processor;storing the prediction equation in the memory of the CGM device; storingcomputer program code in the memory of the CGM device that, whenexecuted by the processor, causes the CGM device to (a) apply a constantvoltage potential to the sensor, measure primary current signalsresulting from the constant voltage potential and store measured primarycurrent signals in the memory; (b) between measurements of primarycurrent signals, apply a probing potential modulation sequence to thesensor, measure probing potential modulation current signals resultingfrom the probing potential modulation sequence and store measuredprobing potential modulation current signals in the memory; (c) for eachprimary current signal, employ the primary current signal, a pluralityof the measured probing potential modulation current signals associatedwith the primary current signal and the stored prediction equation todetermine a glucose value; and (d) communicate determined glucose valuesto a user of the CGM device.

In some embodiments, a continuous glucose monitoring (CGM) device isprovided that includes a wearable portion having a sensor configured toproduce current signals from interstitial fluid; a processor; a memorycoupled to the processor; and transmitter circuitry coupled to theprocessor. The memory includes a prediction equation based on primarycurrent signals generated by application of a constant voltage potentialapplied to a reference sensor, and a plurality of probing potentialmodulation current signals generated by application of a probingpotential modulation sequence applied between primary current signalmeasurements. The memory also includes computer program code storedtherein that, when executed by the processor, causes the CGM device to(a) measure and store a primary current signal using the sensor andmemory of the wearable portion; (b) measure and store a plurality ofprobing potential modulation current signals associated with the primarycurrent signal; (c) employ the primary current signal, the plurality ofprobing potential modulation current signals and the stored predictionequation to compute a glucose value; and (d) communicate the glucosevalue to a user of the CGM device.

In some embodiments, a method of determining analyte concentrationsduring continuous monitoring measurements is provided that includesinserting a biosensor subcutaneously into a subject, the biosensorincluding a counter electrode, a reference electrode and a workingelectrode having a chemical composition configured to oxidize apoint-of-interest analyte; applying a constant voltage to the workingelectrode having the chemical composition so as to generate a continuouscurrent flow from the working electrode; sensing and storing primarycurrent signals from the working electrode into a memory; after sensingeach primary current signal, applying a probing potential modulationsequence to the working electrode, and sensing and storing probingpotential modulation currents generated in response to the probingpotential modulation sequence into the memory; gathering a primarycurrent signal and probing potential modulation currents generated afterthe primary current signal; and employing the gathered primary currentsignal and probing potential modulation currents to compute an analytevalue.

In some embodiments, a method of probing a condition of a continuousanalyte monitoring (CAM) sensor and of calibrating the sensor basedthereon is provided that includes applying an operating voltage to theCAM sensor; probing a condition of the CAM sensor by applying at leastone voltage step greater than the operating voltage and at least onevoltage step less than the operating voltage; measuring output currentsof the CAM sensor in response to the probing; calculating calibrationindices via ratios of the output currents; and calibrating the CAMsensor based on the calibration indices.

In some embodiments, a continuous analyte monitoring (CAM) sensorapparatus is provided that includes a management unit including awireless transmitter/receiver in communication with a wirelesstransmitter coupled to an on-body sensor, the management unit furthercomprising a processor, a memory, and software, wherein the processorand software are operative to (a) apply an operating voltage to theon-body sensor; (b) probe a condition of the on-body sensor by applyingat least one voltage step greater than the operating voltage and atleast one voltage step less than the operating voltage; (c) measureoutput currents of the on-body sensor in response to the probing; (d)calculate calibration indices via ratios of the output currents; and (e)calibrate the on-body sensor based on the calibration indices.

In some embodiments, a method of applying probing potential modulationduring continuous analyte monitoring for determination of analyteconcentration is provided that includes applying a constant operatingvoltage to an analyte sensor during a continuous sensor operation;applying at least one probing potential modulation step different thanthe constant operating voltage in each cycle of the continuous sensoroperation; measuring primary current from the constant operating voltagein each cycle and at least one companion probing potential modulationcurrent in each cycle, responsive to analyte concentration; anddetermining analyte concentration from the primary current and the atleast one companion probing potential modulation current from the atleast one probing potential modulation step.

In some embodiments, a continuous analyte monitoring (CAM) device isprovided that includes a wearable portion having a sensor configured tobe subcutaneously inserted into a subject and to produce current signalsfrom interstitial fluid; a processor; and a memory coupled to theprocessor. The memory includes computer program code stored thereinthat, when executed by the processor, causes the CAM device to: (a)apply a constant voltage to the sensor so as to generate a primarycurrent flow from the sensor; (b) sense and store primary currentsignals generated in response to the constant voltage into the memory;(c) between sensing primary current signals, apply a probing potentialmodulation sequence to the sensor, and sense and store probing potentialmodulation currents generated in response to the probing potentialmodulation sequence into the memory; and (d) employ primary currentsignals and probing potential modulation currents to compute analytevalues over a time period of at least a week. The CAM device does notemploy an in-situ calibration during the time period.

Still other aspects, features, and advantages in accordance with theseand other embodiments of the disclosure may be readily apparent from thefollowing detailed description, the appended claims, and theaccompanying drawings. Accordingly, the drawings and descriptions hereinare to be regarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings, described below, are for illustrative purposes and are notnecessarily drawn to scale. The drawings are not intended to limit thescope of the disclosure in any way.

FIG. 1 illustrates a graph of applied voltage E₀ for a continuousglucose monitoring (CGM) sensor versus time according to one or moreembodiments of the disclosure.

FIG. 2 illustrates a graph of a CGM sensor's output current versus timeaccording to one or more embodiments of the disclosure.

FIG. 3 illustrates a graph of varying CGM sensor input potentialmodulation steps versus time during an initial probing period accordingto one or more embodiments of the disclosure.

FIG. 4 illustrates a graph of varying CGM sensor input potentialmodulation steps versus time during an intermediate probing periodaccording to one or more embodiments of the disclosure.

FIG. 5A illustrates a graph of a current-voltage relationship at andnear a redox plateau for a redox species (mediator) according to one ormore embodiments of the disclosure.

FIG. 5B illustrates a graph of a typical current-time relationship forthe currents before, during, and after the probing potential modulationsshown in FIGS. 3 and 4 according to one or more embodiments of thedisclosure.

FIGS. 6A and 6B illustrate block diagrams of CGM sensor apparatusaccording to one or more embodiments.

FIG. 7A illustrates a graph of applied voltage E₀ for a continuousglucose monitoring (CGM) sensor versus time according to one or moreembodiments of the disclosure.

FIGS. 7B, 7C, 7D, 7E and 7F illustrate example sequences of probingpotential modulations that may be employed in accordance withembodiments provided herein, wherein FIG. 7B illustrates step-wiseprobing potential modulations, FIG. 7C illustrates one step down/upprobing potential modulations in two back-to-back sequences, FIG. 7Dillustrates asymmetrical step probing potential modulations, FIG. 7Eillustrates linear scan/triangle probing potential modulations and FIG.7F illustrates a one-step potential modulation followed by a direct stepreturning to a constant operating potential.

FIG. 8A is an example graph of working electrode (WE) current versustime generated by the probing potential modulations of FIG. 7B duringthe first cycle of the probing potential modulations in accordance withembodiments provided herein.

FIG. 8B is a graph of WE current versus time which illustrates decay ofthe probing potential modulations currents of FIG. 8A in the first 6hours following probing.

FIG. 9A is an example graph of working electrode (WE) current versustime generated by the probing potential modulations of FIG. 7C inresponse to three consecutive cycles of probing potential modulations ata constant glucose concentration in accordance with embodiments providedherein.

FIG. 9B is a graph of WE current in response to the probing potentialmodulations of FIG. 7C taken on seven different days (Day 1—Day 7) at aconstant glucose concentration.

FIG. 10 illustrates a graph of working electrode current versus probingpotential modulation time in accordance with an example embodiment.

FIG. 11A is a graph of working electrode current versus time thatillustrates the temporal response currents of CGM sensors with probingpotential modulations (ppm) and with no probing potential modulations(nppm) in response to different glucose concentrations in differentacetaminophen concentrations as the background signals in accordancewith embodiments provided herein.

FIG. 11B is a graph of predicted glucose concentration versus time forthe WE currents of FIG. 11A based on a simple multi-variate regression.

FIG. 11C is a graph of WE current versus glucose solution concentrationillustrating response lines for linearity at four levels ofacetaminophen with probing potential modulations (ppm) as describedherein.

FIG. 11D is a graph of predicted glucose concentration versus glucosesolution concentration based on the WE currents of FIG. 11C (which weredetermined using probing potential modulations).

FIG. 11E is a graph of WE current versus glucose solution concentrationillustrating response lines for linearity at four levels ofacetaminophen without probing potential modulations (nppm).

FIG. 11F is a graph of predicted glucose concentration versus glucosesolution concentration based on the WE currents of FIG. 11E (which weredetermined without using probing potential modulations).

FIGS. 11G and 11H illustrate initial and ending probing potentialmodulation current correlations, respectively, for an acetaminophenbackground level of 0.2 mg/dL in accordance with embodiments providedherein.

FIG. 12A illustrates a graph of working electrode current versus elapsedtime for three sensors subjected to probing potential modulations(sensor ppm-1, ppm-2 and ppm-3) and one sensor subjected to no probingpotential modulations (sensor nppm-1) in accordance with embodimentsprovided herein.

FIG. 12B illustrates working electrode current versus glucoseconcentration for three sensors (ppm-1, ppm-2, ppm-3) at day-7 inaccordance with embodiments provided herein.

FIG. 12C illustrates working electrode current versus glucoseconcentration for one sensor (sensor ppm-1) at day-1, day-7 and day-14in accordance with embodiments provided herein.

FIG. 12D which illustrates working electrode (primary current) of threesensors with temperature variations during a portion of day-9 oflong-term monitoring in accordance with embodiments provided herein.

FIG. 13A illustrates output glucose values over 17 days where thedifferences in glucose values are reduced and the overall glucoseaccuracy increased through use of probing potential modulations inaccordance with embodiments provided herein.

FIG. 13B shows improvement in three glucose response lines for differentsensors through use of probing potential modulations in accordance withembodiments provided herein.

FIG. 13C illustrates the removal of non-linear characteristics duringwarm up through use of probing potential modulations in accordance withembodiments provided herein.

FIG. 13D illustrates reduction of temperature effects through use ofprobing potential modulations in accordance with embodiments providedherein.

FIGS. 13E and 13F illustrates reduction of sensitivity effects betweendifferent sensors through use of probing potential modulations inaccordance with embodiments provided herein.

FIG. 14A illustrates a high-level block diagram of an example CGM devicein accordance with embodiments provided herein.

FIG. 14B illustrates a high-level block diagram of another example CGMdevice in accordance with embodiments provided herein.

FIG. 15 is a side schematic view of an example glucose sensor inaccordance with embodiments provided herein.

FIG. 16 is a flowchart of an example method of compensating for errorsduring continuous glucose monitoring (CGM) measurements in accordancewith embodiments provided herein.

FIG. 17 is a flowchart of an example method of making a continuousglucose monitoring (CGM) device in accordance with embodiments providedherein.

FIG. 18 is a flowchart of an example method of determining analyteconcentrations during continuous monitoring measurements in accordancewith embodiments provided herein.

FIG. 19 is a flowchart of an example method of probing a condition of acontinuous analyte monitoring (CAM) sensor and of calibrating the sensorbased thereon in accordance with embodiments provided herein.

FIG. 20 is a flowchart of an example method of determining an analyteconcentration from a continuous analyte monitoring (CAM) sensor and ofcalibrating the sensor based thereon in accordance with embodimentsprovided herein.

DETAILED DESCRIPTION

Reference will now be made in detail to example embodiments of thedisclosure, which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts. Features of the variousembodiments described herein may be combined with each other, unlessspecifically noted otherwise.

The terms “voltage,” “potential” and “voltage potential” are usedinterchangeably. “Currents,” “signals” and “current signals” are alsoused interchangeably, as are “continuous analyte monitoring” and“continuous analyte sensing.” As used herein, probing potentialmodulations refer to intentional changes made periodically to theotherwise constant voltage potential applied to a sensor duringcontinuous analyte sensing, such as application of probing potentialsteps, pulses, or other potential modulations to the sensor. Primarydata points or primary currents refer to measurements of current signalsgenerated in response to an analyte at a constant voltage potentialapplied to a sensor during continuous analyte sensing. Probing potentialmodulation (ppm) currents refer to measurements of current signalsgenerated in response to probing potential modulations applied to thesensor during continuous analyte sensing. Reference sensors refer tosensors used to generate primary data points and ppm currents inresponse to reference glucose concentrations represented by BGMreadings, for example (e.g., primary currents and ppm currents measuredfor the purpose of determining prediction equations that aresubsequently stored in a continuous analyte monitoring (CAM) device andused during continuous analyte sensing to determine analyteconcentrations).

For sensors deployed in a non-whole blood environment with relativelyconstant temperatures, such as sensors used in a continuous in-vivosensing operation, sensor error may be related to the sensor's short andlong-term sensitivity and method of calibration thereafter. There areseveral problems/issues associated with such a continuous sensingoperation: (1) the long break-in (warmup) time, (2) the factory orin-situ calibration, and (3) the change of sensitivity during thecontinuous sensing operation. These issues/problems are seeminglyrelated to the sensor sensitivity as expressed in the initial decay(break-in/warmup time), the change of sensitivity due to thesusceptibility of the sensor to the environment while in sensorproduction, and the environments/conditions in which the sensor isthereafter deployed.

According to one or more embodiments of the disclosure, apparatus andmethods are operative to probe an initial starting condition of acontinuous sensor operation for a sample analyte and to probe the sensorcondition at any point thereafter during the sensor's continuous sensingoperation. The results of the probing sequence may include calibrationindices, determined from electrical signals obtained from the probingsequence, that indicate whether in-situ adjustment of the sensor'scalibration either initially and/or at random check points is needed. Insome embodiments, the output of the probing method and its calibrationindices may provide the in-situ calibration internally for thecontinuous sensor operation (and/or in embodiments described below, theprobing method may reduce and/or eliminate the need for in-situcalibrations).

Embodiments described herein include systems and methods for applyingprobing potential modulations on top of the otherwise constant voltageapplied to an analyte sensor. The terms voltage, potential, and voltagepotential are used herein interchangeably.

Methods are provided of formulating parameters for a prediction equationthat may be employed to accurately determine analyte concentrationscontinuously from an analyte sensor. Furthermore, a method of andapparatus for determining analyte concentrations are provided with theuse of probing potential modulation (ppm) self-sufficient signals (e.g.,working electrode currents resulting from the application of probingpotential modulations). Such methods and apparatus may allow analyteconcentration determinations while (1) overcoming the effects ofdifferent background interfering signals, (2) levelling or removing theeffects of different sensor sensitivities, (3) shortening the warmuptime at the beginning of a (long-term) continuous monitoring process,(4) correcting sensor sensitivity changes over the continuous monitoringprocess, and/or (5) correcting the effects of temperature on sensoroutput currents. These and other embodiments are described below withreference to FIGS. 1-20.

For a continuous glucose monitoring (CGM) biosensor, which is usuallyoperated with a constant applied voltage, the currents from the mediatorare measured continuously as a result of the enzyme oxidation of thetarget analyte glucose. In practice, currents are typically measured orsensed every 3 to 15 minutes or at another regular interval despitebeing referred to as continuous. There is an initial break-in time whenthe CGM sensor is first inserted/implanted into a user, which may lastfrom 30 minutes to several hours. Once the CGM sensor is broken-in, itssensitivity may still change for various reasons. Thus, there is a needto sense the sensor's operating condition during its initial and afterbreak-in times to identify any changes in its sensitivity.

The CGM sensor operation starts with the applied voltage E₀ after it isinserted/implanted subcutaneously into a user. The applied voltage E₀ isusually at a point on the redox plateau of the mediator. For the naturalmediator of oxygen with the enzyme of glucose oxidase, the oxidationplateau of hydrogen peroxide H₂O₂ (the oxidation product of the enzymereaction) ranges from about 0.5 to 0.8 volts versus an Ag/AgCl referenceelectrode in a media of about 100-150 mM chloride concentration. Theoperation potential for the glucose sensor may be set at 0.55-0.7 volts,which is within the plateau region. FIG. 1 shows such a fixed potential(applied voltage) E₀, while FIG. 2 shows the typical behavior of thesensor's output currents at the initial state with decay and thesettle-in state, wherein the sensor records the up/down changes of theglucose during the deployment. Specifically, FIG. 1 illustrates a graph100 of working electrode voltage versus time during a continuous sensingoperation, while FIG. 2 illustrates a graph 200 of working electrodecurrent versus time.

FIG. 1 also shows the positions in time for the initial probing and theintermediate probing using the probing potential modulations. Exampleprobing potential modulations are further illustrated in FIGS. 3 and 4.The probing potential modulations and their adjacent cluster potentialsteps may be further defined as follows:

Probing potentials: For the initial probing of the sensorcondition/environment, in some embodiments, probing may start 0-5minutes after sensor insertion and the initial applied voltage. Otherinitial probing start times may be used. In some embodiments, theprobing potential modulations may include at least one forward potentialstep from the base potential E₀. That is, the forward step potential E₁is higher than E₀ with ΔE_(1,0)=E₁−E₀>0 and in the order of 0.05-0.3volts. The probing potential modulations also may include at least onereverse potential step E₂ such that ΔE_(2,0)=E₂−E₀ is in the order of−0.05 to −0.5 volts; that is, E₂ is substantially lower than E₁ and E₀,where ΔE_(2,1)=E₂−E₁<0 and ΔE_(2,0)=E₂−E₀<0.

FIGS. 5A and 5B illustrate example relative potential steps (graph 500A)and the typical current behaviors (graph 500B) during the back/forthpotential steps described above. The potential E₂ is designed to set themediator in a partial reduced state. The ratio of the potential stepending currents i_(1,t)/i_(2,t) may provide an assessment of the sensorcondition and sensitivity. That is, i_(1,t) (i_(0,t), i_(3,t) as well)provides the diffusion limited current from the oxidizing of the reducedstate of the mediator, while i_(2,t) provides the kinetic current whichis related to the sensor sensitivity. Additional potential steps mayinclude a forward potential step E₃ higher than or equal to E₀, butlower than E₁, and another reverse potential step to return to E₀ fromE₃. This probing potential sequence is designed to have a minimalperturbation to the on-going current monitoring at a fixed potential E₀after the final probing potential step returns to E₀. After the probingpotential modulations, the regular current record frequency may resume.

Probing period and rest period: In some embodiments, the timing of oneprobing sequence including the multiple probing potential modulations(potential steps in this example) may be in an order of 5-100 secondswhere each potential step may have a duration of 1-20 seconds with equalor unequal step size for the individual steps. This one probing periodmay be separated by a rest period of 1-30 minutes, for example. Anexample of such a probing scheme may be represented by a probing periodof 30 sec for a probing group of 3-5 steps, separated by a rest periodof 14.5 minutes, with one probing cycle being in 15 minutes. While thelong-term sensor response currents may be measured at a frequency ofevery 1-15 minutes, the current sampling interval of the probingpotential modulations may be in the order of 0.1-5 sec, depending on thestep duration of the probing potential modulations.

Current decay constant of the probing potential modulations: Exampleprobing potential modulations positions and their typical current decaybehavior within their potential steps are shown in FIGS. 5A and 5B. Theprobing potential modulation (PPM) currents are, in general,proportional to the magnitude of the step potential by:

$\begin{matrix}{i = {\frac{\Delta E}{R_{S}}e^{{{- t}/R_{s}}C_{d}}}} & (1)\end{matrix}$

where ΔE is the potential step; R_(S) is the solution resistance betweenthe working electrode and the reference electrode or the combinedreference/counter electrode; C_(d) is electrode surface capacitance; andt is the time after the initial step potential. After the initialcurrent spike characterized by ΔE/R_(S), the current will decayapproximately exponentially (exp(−t/R_(S)C_(d))). Thus, if ΔE>0, thestep potential current will be positive and if ΔE<0, the step potentialcurrent will be negative. Such behaviors are depicted in FIG. 5B for thefour potential steps. For each probing potential modulation, there is adecay characteristic of the sensor electrode, the enzyme/membraneencapsulation and the sensor's environment. This decay may becharacterized by the decay constant:

$\begin{matrix}{K_{p} = \frac{{\ln \; i_{n,o}} - {\ln \; i_{n,t}}}{{\ln \; t_{n,o}} - {\ln \; t_{n,t}}}} & (2)\end{matrix}$

where i_(n,0) denotes the initial current of a step at E_(n) (n=1, 2, 3,. . . ) and i_(n,t) denotes the ending current of a step at time t foreach potential step. It may also be defined by the ratios ofi_(1,t)/i_(1,0), i_(2,t)/i_(2,0), i_(3,t)/i_(3,0), and i_(4,t)/i_(4,0),where i_(n,0) is the initial step current and i_(n,t) is the ending stepcurrent at time t of each potential step. These decay constants mayreflect the sensor sensitivity changes, or the sensor's enzyme/membranecondition changes during the break-in time.

Ratios of the potential step ending currents: The potential step endingcurrents from E₀, E₁, and E₃ should be close to each other aftersufficient decay of the currents. This occurs because E₀, E₁, and E₃ areat the redox plateau with the diffusion limited current. However, thepotential step ending current for E₂ may be substantially smaller thanthose from E₀, E₁, and E_(3,) because E₂ is in a region with currentmuch lower than that of the diffusion limited currents in the redoxplateau. In particular, the ratio of i_(1,t)/i_(2,t) may indicate therelative sensitivity of the sensor at the nearby time, and so mayi_(3,t)/i_(2,t). Comparison of these ratios to the average ratios ofi_(1,t)/i_(2,t) and i_(3,t)/i_(2,t) from the factory calibration mayprovide the relative sensitivity of the sensor and thus the basis for anin-situ calibration. This ratio may also provide the sensor conditionsat various stages. Accordingly, the factory calibration may be based notonly on the response curve for the sensor (e.g., calibration constantsets of slope and intercept, the coefficients of a polynomial equationthat relates sensor current signals and analyte concentrations, etc.),but also based on the calibration indices obtained from probingpotential modulations. While the calibration constants of slope andintercept may only be obtained with in-vitro dosing of referenceconcentrations of the analyte, the calibration indices described hereinmay be generated through in-situ potential modulation (or other types ofprobing potential modulations) with potential steps higher and lower theoperation potential E₀, which are added as additional calibrationelements to the factory calibration. For example, in some embodiments,calibration constants may include multiple sets of slope and intercept,and calibration indices may be correlated with the different sets ofslope and intercept. These constants and indices may be stored in thesensor system's memory for in-situ calibration during the sensoroperation.

Initial probing: If a probing scheme is applied every 15 minutes, andwhile returning to the normal applied voltage E₀ afterwards, the firsthour will provide four different sets of indices characteristic of thesensor, where different calibration constants may be applied to predictthe glucose concentrations within such a short period. As probingindices generated from the four consecutive probing potentialmodulations change along the break-in time, a trend for the initialdecay currents may be established to predict the following currentbehavior and thus provide the glucose determination based on thetrending of the probing indices and the factory calibration constants,even when the general current behavior is still decaying. This approachmay help to shorten the overall sensor break-in/warmup time from about 3hours to 1 hour in some embodiments. Initial probing may be performed atother time periods (e.g., time periods less than every 15 minutes orgreater than every 15 minutes so that fewer or more than four differentsets of indices characteristic of the sensor may be obtained).

Intermediate probing: In some embodiments, the probing potentialmodulations may be applied periodically on a daily basis to provide ananchor to the long-term monitoring currents. For example, one or moresets of probing potential modulations may be applied when the sensor isat a relatively low variation state. When the probing indices generatedfrom the probing potential modulations show a change in the sensorsensitivity, sensitivity adjustments may be applied to correct for thechange. This is a step of in-situ calibration (internal calibration).

Sensor system intelligence: The probing potential modulations scheme maybe applied as a routine such as initially, or may be appliedperiodically. The application of probing potential modulations mayemploy some built-in intelligence (e.g., software operating on amicroprocessor of a management unit of a CGM sensor) for initiating thepotential modulations and performing the calculations of the indices tobe used for in-situ sensor calibration.

FIGS. 6A and 6B illustrate CGM apparatus 600 according to one or moreembodiments. CGM apparatus 600 includes a management unit 602 that has awireless transmitter/receiver unit 604, a wireless transmitter 605coupled to an on-body sensor 607, which is received in a sensor pod 609mountable to a user's body 611 (e.g., torso). Wireless transmitter 605communicates sensor readings and other data to wirelesstransmitter/receiver unit 604. A cannula, needle, or sensor component613 (e.g., an analyte sensor) is inserted into the user's body 611through known means, such as use of an insertion set and interfaces withthe on-body sensor 607 to allow substantially continuous sensing of aglucose level in the user's interstitial fluid. The management unit 602has a housing 606, a display screen 608 that displays glucose readingsand/or trends, and a user interface 610 that may include a plurality ofbuttons for controlling various features of the management unit 602. Asshown in FIG. 6B, management unit 602 also includes an antenna 612, aprocessor 614 (which may be, e.g., a microprocessor), a memory 616,software 618, a rechargeable battery 620, a battery charger 622, ananalog interface 624, and a cable connector 626. The processor 614,memory 616, and software 618 are operative to perform the probing of aCGM sensor (e.g., on-body sensor 607) with respect to its sensitivity oroperating conditions, the extracting and storing of sensitivity indicesfor the CGM sensor's operating conditions, and the in-situ calibrationadjustment as needed of CGM apparatus 600 as described above.

Embodiments described herein employ probing potential modulations asperiodic perturbations to the otherwise constant voltage potentialapplied to the working electrode of a subcutaneous biosensor in acontinuous sensing operation (e.g., for monitoring biological sampleanalyte such as glucose). While the previous embodiments describe use ofprobing potential modulations during an initial time period afterinsertion of a sensor, and at intermediate time periods, probingpotential modulations may be used at other time periods. For example,during a continuous sensing operation, such as continuous glucosemonitoring, sensor working electrode current is typically sampled every3-15 minutes (or at some other frequency) for glucose valuedeterminations. These current measurements represent the primarycurrents and/or primary data points used for analyte determinationsduring continuous sensing operation. In some embodiments, periodiccycles of probing potential modulations may be employed after eachprimary current measurement so that a group of self-sufficient currentsaccompanies each primary data point with information about thesensor/electrode status and/or condition.

Probing potential modulations may include one or more steps in potentialthat are different than the constant voltage potential normally usedduring continuous analyte monitoring. For example, probing potentialmodulations may include a first potential step above or below theconstant voltage potential, a first potential step above or below theconstant voltage potential and then a potential step returning to theconstant voltage potential, a series of potential steps above and/orbelow the constant voltage potential, voltage steps, voltage pulses,pulses of the same or different durations, square waves, sine waves,triangular waves, or any other potential modulations.

As described, conventional biosensors used in continuous analyte sensingare operated by applying a constant potential to the working electrode(WE) of the sensor. Under this condition, the currents from the WE arerecorded periodically (e.g., every 3-15 minutes or at some other timeinterval). In this way, biosensors generate currents that are onlyattributable to changes in analyte concentrations, not changes inapplied potential. That is, non-steady-state currents associated withthe application of different potentials are not present. While thisapproach simplifies the continuous sensing operation, the currentsignals in the data stream from application of a constant potential tothe sensor provide minimum information about the sensorstatus/condition. That is, sensor current signals from application of aconstant potential to a sensor provide little information relevant toissues associated with long-term continuous monitoring by the sensor,such as lot-to-lot sensitivity variations, the long warmup time due toinitial signal decay, sensor sensitivity changes over a long-termmonitoring process, effects from varying background interfering signals,or the like.

Embodiments described herein include systems and methods for applyingprobing potential modulations on top of the otherwise constant voltageapplied to an analyte sensor. Methods are provided for formulatingparameters for a prediction equation that may be employed to accuratelydetermine analyte concentrations continuously from an analyte sensor.Furthermore, methods of and systems for determining analyteconcentrations with the use of probing potential modulation (ppm)self-sufficient signals are provided. Such methods and systems may allowanalyte concentration determinations while (1) overcoming the effects ofdifferent background interfering signals, (2) levelling or removing theeffects of different sensor sensitivities, (3) shortening the warmuptime at the beginning of a (long-term) continuous monitoring process,(4) correcting sensor sensitivity changes over the continuous monitoringprocess, and/or (5) correcting the effects of temperature on sensoroutput currents. These and other embodiments are described below withreference to FIGS. 7A-19.

FIG. 7A illustrates a graph of applied voltage E₀ for a continuousglucose monitoring (CGM) sensor versus time according to one or moreembodiments of the disclosure. Example times at which measurements ofprimary data points may be made, and subsequent probing potentialmodulations may be applied, are shown. As shown in FIG. 7A, the constantvoltage potential E₀ applied to the working electrode of an analytesensor may be about 0.55 volts in this example. Other voltage potentialsmay be used. FIG. 7A shows an example of a typical cycle of the primarydata points taken at a constant applied voltage. Primary data points arethe data points measured or sampled at a constant applied voltage and atregular intervals, such as 3-15 minutes, during continuous glucosemonitoring and used to compute glucose values for a user. Primary datapoints may be working electrode currents measured for an analyte sensorduring continuous analyte monitoring, for example. FIG. 7A does not showprimary data points, but the time and voltage at which each primary datapoint is measured. For example, circle 702 in FIG. 7A represents thetime/voltage (3 minutes/0.55 volts) at which a first primary data point(e.g., a first working electrode current) is measured for a sensorbiased at a voltage of E₀. Likewise, circle 704 in FIG. 7A representsthe time/voltage (6 minutes/0.55 volts) at which a second primary datapoint (e.g., second working electrode current) is measured for a sensorbiased at a voltage of E₀.

FIGS. 7B, 7C, 7D, 7E and 7F illustrate example sequences of probingpotential modulations that may be employed in accordance withembodiments provided herein. The triangles on top of the potentialprofiles denote the times at which probing potential modulation currentsare measured, as examples in this embodiment. For example, FIG. 7Billustrates step-wise probing potential modulations, FIG. 7C illustratesone step down/up probing potential modulations in two back-to-backsequences, FIG. 7D illustrates asymmetrical step probing potentialmodulations, FIG. 7E illustrates linear scan/triangle probing potentialmodulations, and FIG. 7F illustrates a one-step potential modulationfollowed by a direct step returning to the constant operating potential,respectively. Other probing potential modulations types may be used. Inthese figures, “primary volt” denotes the constant applied potentialunder the normal sensor operation; “primary data” denotes the timing ofthe primary data points (e.g., current signals) recorded periodically asbeing indicative of the analyte concentration; “probing volt” denotesthe probing potential modulation potentials applied as perturbation tothe primary/constant applied potential; and “probing data” denotes thetiming of the current signals generated by probing potential modulationsand recorded at a specified sampling rate. While FIGS. 7B-7E illustrateprobing potential modulations of 4 or more steps, it will be understoodthat few or more probing potential modulation steps may be used (e.g.,1, 2, 3, 4, 5, 6, 7, 8, etc.).

Probing potential modulations may be applied before or after primarydata points are measured. In the embodiments of FIGS. 7B-7E, probingpotential modulations are applied to a sensor immediately after eachprimary data point is measured (e.g., after a primary data point ismeasured at 3 minutes, 6 minutes, 9 minutes, etc.).

Example primary data points and probing potential modulations are nowdescribed. While described primarily with regard to voltage pulses orvoltage steps, it will be understood that other types of probingpotential modulations may be used as previously described. Withreference to FIG. 7A, 0.55V is applied to the working electrode of ananalyte sensor (e.g., a glucose sensor having a chemical compositionsuch as glucose oxidase for oxidizing/converting glucose to a productsuch as H₂O₂) relative to a reference electrode such as Ag/AgCl. Othersensor types and/or electrode materials may be used. Continuous currentflow through the working electrode is measured at a fixed samplingfrequency of 3 minutes periodically, as the primary data points/currentsignals. Other sampling frequencies may be used.

After each primary data point is measured, probing potential modulationsmay be applied to the working electrode to probe sensor/electrode statusand/or condition, for example. In the embodiment of FIG. 7B-7F, thebelow described probing potential modulations are applied after eachprimary data point is measured. Specifically, in FIGS. 7B-7F, probingpotential modulations are employed after the primary data point ismeasured at 3 minutes (circle 702) and 6 minutes (circle 704). Similarprobing potential modulations may be employed after each primary datapoint is measured (e.g., after 0, 3, 6, 9, 12, 15, 18, etc., minutes).As mentioned, in other embodiments, probing potential modulations may beapplied prior to measuring a primary data point (e.g., assuming theprimary data point is not measured until probing potential modulationcurrent has decayed away). In some embodiments, probing potentialmodulations maybe applied prior to and after measuring a primary datapoint.

With reference to FIG. 7B, six voltage steps (Steps 1-6) may be employedafter each primary data point is measured. In the embodiment shown, eachstep lasts 6 seconds and the resulting working electrode current signalis measured every 2 seconds (resulting in the measurement of 3 currentsignals per potential step). Other steps in voltage, step durationsand/or sampling rates may be used.

Step 1: 0.55V=>0.6V;

Step 2: 0.6V=>0.45V;

Step 3: 0.45V=>0.3V;

Step 4: 0.3V=>0.45V;

Step 5: 0.45V=>0.6V;

Step 6: 0.6V=>0.55V;

Following Step 6, a constant voltage of 0.55V is resumed until the nextprimary data point is measured and the probing potential modulationsequence is repeated.

With reference to FIG. 7C, six voltage steps (Steps 1-6) may be employedafter each primary data point is measured. In the embodiment shown, eachstep lasts 6 seconds and the resulting working electrode current signalis measured every 2 seconds (resulting in the measurement of 3 currentsignals per potential step). Other steps in voltage, step durationsand/or sampling rates may be used.

Step 1: 0.55V=>0.6V;

Step 2: 0.6V=>0.25V;

Step 3: 0.25V=>0.6V;

Step 4: 0.6V=>0.45V;

Step 5: 0.45V=>0.6V;

Step 6: 0.6V=>0.55V;

Following Step 6, a constant voltage of 0.55V is resumed until the nextprimary data point is measured and the probing potential modulationsequence is repeated.

With reference to FIG. 7D, four voltage steps (Steps 1-4) may beemployed after each primary data point is measured. In the embodimentshown, each step lasts 6 seconds and the resulting working electrodecurrent signal is measured every 2 seconds (resulting in the measurementof 3 current signals per potential step). Other steps in voltage, stepdurations and/or sampling rates may be used.

Step 1: 0.55V=>0.65V;

Step 2: 0.65V=>0.35V;

Step 3: 0.35V=>0.6V;

Step 4: 0.6V=>0.55V;

Following Step 4, a constant voltage of 0.55V is resumed until the nextprimary data point is measured and the probing potential modulationsequence is repeated.

With reference to FIG. 7E, six linearly changing voltage steps (Steps1-6) may be employed after each primary data point is measured. In theembodiment shown, each step lasts 6 seconds and the resulting workingelectrode current signal is measured every 2 seconds (resulting in themeasurement of 3 current signals per potential step). Other steps involtage, step durations and/or sampling rates may be used.

Step 1: Linear scan from 0.55V=>0.6V, scan rate of 0.00833 V/sec;

Step 2: Linear scan from 0.6V=>0.25V, scan rate of 0.05833 V/sec;

Step 3: Linear scan from 0.25V=>0.6V, scan rate of 0.05833 V/sec;

Step 4: Linear scan from 0.6V=>0.45V, scan rate of 0.025 V/sec;

Step 5: Linear scan from 0.45V=>0.6V, scan rate of 0.025 V/sec;

Step 6: Linear scan from 0.6V=>0.55V, scan rate of 0.00833 V/sec;

Following Step 6, a constant voltage of 0.55V is resumed until the nextprimary data point is measured and the probing potential modulationsequence is repeated.

For the probing potential modulation examples above, other timing and/orapplied voltages may be used. For example, other potential stepsequences for different biosensor mediators may be devised.

With reference to FIG. 7F, one potential modulation step is applied tothe working electrode, followed by directly returning to the originalconstant voltage of 0.55V.

FIG. 8A is an example graph of working electrode (WE) current versustime generated by the probing potential modulations of FIG. 7B duringthe first cycle of the probing potential modulations in accordance withembodiments provided herein. In this example, a CGM glucose sensor wasplaced in a 100 mg/dL glucose solution. FIG. 8B is a graph of WE currentversus time which illustrates decay of the probing potential modulationcurrents of FIG. 8A in the first 6 hours. A sample rate of1-second/point was employed throughout. FIG. 8B provides an overall viewof all probing potential modulation (PPM) currents where the outercontour of the profile shows a clear decay behavior, indicating that PPMcurrents have embedded information about the decay nature of sensorcurrents after sensor insertion and activation. Because PPM currentshave embedded information about sensor current decay, PPM currents maybe used as self-sufficient information to correct for the transientnature of sensor sensitivity. Warmup time may then be shortened, asshown below, instead of waiting for a sensor to reach a meta-steadystate.

FIG. 9A is an example graph of working electrode (WE) current versustime generated by the probing potential modulations of FIG. 7C inresponse to three consecutive cycles of probing potential modulations inaccordance with embodiments provided herein. In this example, a CGMglucose sensor was placed in a 100 mg/dL glucose solution. FIG. 9B is agraph of WE current in response to the probing potential modulations ofFIG. 7C taken on seven different days (Day 1-Day 7). A sample rate of1-second/point was employed throughout. A similar WE current response isobserved throughout Day 1 and from Day 1 to Day 7.

As shown in the above two examples of FIGS. 8A, 8B, 9A and 9B, in someembodiments, the current signals of the probing region and thenon-probing potential modulation region may be measured at a fixedsampling rate such as 1-sec/point. In other embodiments, differentsampling rates may be employed. For example, the primary data points maybe measured at a slower sample rate of 1, 2, 3, 5, 10 or 15 minuteswhile the probing potential modulation currents may be measured at asample rate of 0.5, 1, 2, 3 or 5 seconds within each potential step. Theprimary data points may be further measured as an average of multiplesignals at the constant applied voltage within a close time range of theperiodic sample time (e.g., every 3 minutes) such as within 60, 30, 20,10, or 5 seconds, to reduce the random signal noise. The same may bedone for the probing potential modulation currents within 0.1, 0.2 or0.5 sec of the periodic sample time (e.g., every 1 second), depending onthe A-D conversion speed. Other sample rates and/or sampling schemes maybe used.

It can be seen from FIGS. 8A, 8B, 9A and 9B that the magnitudes of theprobing potential modulation currents (the “ppm” or “PPM” currents) aresubstantially larger than the otherwise steady-state currents (thenon-probing or “nppm” or “NPPM” currents measured without probingpotential modulation perturbations). Without wishing to be bound by anytheory, the goal of the probing potential modulations is to createperturbated output currents in a short time period to obtain sensorstatus/condition information while primary data points are measuredwithout the effects of the probing potential modulations. That is,primary data points are measured when the WE current has returned to theotherwise flat current profile generated at the constant voltage E₀. Itis postulated that there is a wealth of information about thesensor/electrode status/condition embedded in the ppm currents generatedby the probing potential modulations. As mentioned, the probingpotential modulations may be applied before or after a primary datapoint is measured. In some embodiments, probing potential modulationoutput currents may be generated on at least one side of the otherwiseflat current profile measured as a primary data point. In otherembodiments, probing potential modulation output currents may begenerated on both sides of the otherwise flat current profile measuredas a primary data point. In yet other embodiments, negative and positiveprobing potential modulation output currents may be generated on bothsides of the otherwise flat current profile measured as a primary datapoint.

Description of the Probing Potential Modulation Currents

FIG. 10 illustrates a graph of working electrode current versus probingpotential modulation time in accordance with an example embodiment. Withreference to FIG. 10, the probing potential modulation steps used togenerate the working electrode currents of FIG. 10 are in the followingsequence: a forward step from the fixed/constant voltage (e.g., 0.55volts), followed by two reversed steps, followed by two forward steps,and finalized with a small reversed step to facilitate the returning tothe constant potential (e.g., 0.55 Volt), similar to the probingpotential modulation steps of FIG. 7B. The probing potential modulationoutput currents may follow the primary current recorded at the constantpotential of 0.55 Volt in one cycle, or the primary data point (asdescribed with reference to FIG. 7B, for example). Probing potentialmodulation (ppm) output current and non-probing (nppm) output current(e.g., the current due to the constant potential without probingpotential modulations) are labelled in FIG. 10. In the embodiment shown,both the ppm current and the nppm current are measured at the samesampling rate of 2-seconds/point (resulting in the measurement of 3current signals per voltage step, such as i11, i12, i13, i21, i22, i23,etc.). Other sampling rates may be used. In the figures, i10, i11, i12,i13, 121, i22, i23, etc., may be referred to as i1.0, i1.1, i1.2, i2.1,i2.2, i2.3, etc.

Since the probing potential modulations are applied periodically (e.g.,after a primary data point is measured) as a potential perturbation tothe otherwise constant potential applied to the working electrode, eachprimary data point may be accompanied by a group of ppm currents. Insome embodiments, the period of applying the probing potentialmodulations may vary from 1 minute up to several hours, and in someembodiments, from about 3-15 minutes when periodic analyte concentrationis to be reported. In one particular embodiment, the period of applyingprobing potential modulations is 3 minutes (e.g., after each primarydata point is measured at 3 minute intervals). The minimum time betweenprimary data points may be set based on how soon the output current fromthe constant potential stabilizes after each probing potentialmodulation cycle, for example.

As an example, accuracy improvement from use of probing potentialmodulations is demonstrated by a data set from an in-vitro laboratorystudy in which CGM sensors were submerged in glucose solutions havingfour different levels of acetaminophen representing background signals:0.2 mg/dL, 0.6 mg/dL, 1.2 mg/dL and 1.8 mg/dL. These four levels ofacetaminophen are used to simulate different background signals frominterference species using acetaminophen as the surrogate molecule forspecies oxidizable at 0.55 V, as well as the subsequent outcome ofcorrecting the effects of different background signals using PPMcurrents. The acetaminophen concentration of 0.2 mg/dL is considered tobe equivalent to a normal level of interfering background signal while0.6 mg/dL is considered to be a high level. The 1.2 mg/dL and 1.8 mg/dLacetaminophen concentrations are considered to be extremely high levels.One linearity run at five levels of glucose concentration, 50, 100, 200,300, 450 mg/dL, was carried out for each level of backgroundacetaminophen.

FIG. 11A is a graph of working electrode currents versus time thatillustrates the temporal response currents (the primary data points) ofCGM sensors with probing potential modulations (ppm) (curve 1102) andwith no probing potential modulations (nppm) (curve 1104) for theabove-described samples, in accordance with embodiments provided herein.This graph shows that the current profile of the primary data pointswith ppm behaves similar to the current profile of the primary datapoints with no ppm, except that the sensors employed had differentsensitivities. This behavior indicates once again that probing potentialmodulations do not affect the primary data points, but provideadditional information about sensors conditions/changes which will befurther described below. FIG. 11B is a graph of predicted glucoseconcentrations versus time for the WE currents of FIG. 11A based on asimple multi-variate regression. FIG. 11C is a graph of WE currentversus glucose solution concentration illustrating response lines forlinearity at four levels of acetaminophen with probing potentialmodulations (ppm) as described herein. FIG. 11D is a graph of predictedglucose concentration versus glucose solution concentration based on theWE currents of FIG. 11C (which were determined using probing potentialmodulations). FIG. 11E is a graph of WE current versus glucose solutionconcentration illustrating response lines for linearity at four levelsof acetaminophen without probing potential modulations (nppm). FIG. 11Fis a graph of predicted glucose concentration versus glucose solutionconcentration based on the WE currents of FIG. 11E (which weredetermined without using ppm currents). Linear regression equations areshown progressively from lower to upper lines in FIGS. 11C, 11D, 11E and11F for the four lines corresponding to the four levels of acetaminophen(AA) with increasing intercepts representing the influence from theincreasing acetaminophen level.

Table 1 summarizes the response lines (slopes and intercepts) from FIGS.11C, 11D, 11E and 11F in terms of primary data points and calculatedglucose for a sensor employing PPMs and a sensor employing no PPMs. Theoutput glucose values for the probing potential modulation (PPM) andno-PPM methods are calculated from predictive equations (describedbelow) derived from all data of four levels of acetaminophen.

TABLE 1 Summary of effects of added acetaminophen (AA) Response CurrentsResponse Glucose Levels AA (mg/dL) Slope Intercept Slope Intercept PPM 10.2 0.1864 5.7371 1.0119 −1.0014 2 0.6 0.1856 9.6327 0.9922 2.365 3 1.20.1817 15.374 0.974 4.2875 4 1.8 0.1772 21.25 0.9779 6.0893 Average0.1827 12.9985 0.9890 2.9351 No 1 0.2 0.1476 5.6165 0.9078 −8.6079 PPM 20.6 0.1431 10.007 0.8801 18.39 3 1.2 0.1429 14.405 0.8785 45.37 4 1.80.1376 20.119 0.8459 80.578 Average 0.1428 12.5369 0.8781 33.9325

It can be seen from Table 1 that while the average response slopes forthe two sensors may be a matter of individual sensor sensitivity frommanufacturing, which may vary, the effect of added acetaminophen (AA) asan interferent background substance produces a substantial increase inthe intercept. The effect on the intercept is large but similar for thePPM and No PPM data sets. Using the PPM currents as part of the inputinformation to the predictive equation for glucose, the correlation ofthe output glucose against the reference glucose (represented by theslope, also referred to as “correlation slope”) is approaching 1, whichis to be expected. If the predictive equation is based on the data fromlevels 1 and 2, then the maximum effect of the added acetaminophen iswithin ±6 mg/dL. This level of effect on the output glucose is wellwithin the influence of other factors; that is, the effect is very smallcompared to the influence from other factors, such as daily sensitivitychange. On the other hand, the correlation slope of the output glucosefor the No-PPM data is less than 1, reduced by at least 10% due to theoverall weighing effect of low and high acetaminophen currents. That is,when a statistical average line is drawn across the four data sets(acetaminophen levels 1, 2, 3 and 4), the correlation slope is affectedby the elevated intercept from the level 4 acetaminophen data set. Thelarge effect of the added acetaminophen cannot be removed withoutadditional information such as PPM currents. As such, the maximum effecton the output glucose when no PPM currents are employed is as much as 80mg/dL in error, which would be 110% for glucose at 70 mg/dL or 80% forglucose at 100 mg/dL.

The primary data point profiles recorded at the 3-minute period areshown in FIG. 11A with five glucose levels in each of four acetaminophenbackgrounds. For probing potential modulation (ppm) data from theprobing potential modulations, only the primary data points are shown(in the same format as the no-probing potential modulation (nppm) datapoints). That is, only the responses of the constant operating voltagegenerated currents to the stimuli of the acetaminophen and glucose areshown for the ppm data in FIG. 11A. The two sensors are shown to havedifferent sensitivities, which is related to the individual sensorsensitivity from manufacturing, instead of being related to the PPM andno-PPM methods. In addition, the effect of the increasing backgroundacetaminophen from the normal level of 0.2 mg/dL to the highest level of1.8 mg/dL is visible at the low level of glucose at 50 mg/dL. The twodata sets of probing potential modulation (ppm) and no-probing potentialmodulation (nppm) data were analyzed with a simple multi-variateregression using the glucose concentrations as the targets and theprimary data point, and in case where ppm data was present, the 18 ppmcurrents that followed the primary data point (3 per voltage step), asthe inputs to the regression to derive a prediction equation for theprobing potential modulation data (ppm) and the non-probing potentialmodulation data (nppm). A statistics software such as Minitab softwareavailable from Minitab, LLC of State College, Pa., may be employed forregression analysis, for example.

For the probing potential modulation (ppm) data, the effect ofincreasing the background acetaminophen is most obvious in theintercepts for the response lines of the four linearity runs with aminor effect on the slopes being observed as shown in FIG. 11C. Thepredicted glucose plot in FIG. 11D, however, shows the collapse of thefour lines virtually into one where the prediction equation incorporatesa large number of probing potential modulation (ppm) currents. On theother hand, the effect of different background acetaminophen levels wasunable to be overcome by the primary data points only (without probingpotential modulation data) where the four separate lines in the glucosesignal response plot of FIG. 11E are converted to four separate lines ofpredicted glucose in FIG. 11F. This comparison shows that the probingpotential modulation currents provide rich information to correct forthe effect due to background signal variations while the currents from aconstant applied voltage are highly susceptible to the background signalvariations.

For the no-probing potential modulation (nppm) data, the primary datapoints alone cannot overcome the substantial changes in the backgroundacetaminophen concentrations, thus giving output glucose values withsignificant influence from the background interfering species. For theprobing potential modulation (ppm) data, there is a group of probingpotential modulation (ppm) currents accompanying each primary datapoint. Review of the glucose prediction equation by regression indicatesthat 11 out of the 19 inputs (primary data point current from theapplied constant potential and 18 probing potential modulation (ppm)currents) are selected as significant in the glucose predictionequation. The probing potential modulation (ppm) currents ofsignificance were from voltage steps 1 through 5. Thus, these probingpotential modulation (ppm) currents are self-sufficient informationcurrents which have subtle correlations with different effects for thesensor and/or the working electrode output currents. Subsequently, theseprobing potential modulation (ppm) currents help formulate a predictionequation for glucose that corrects for different background signals fromfour levels of acetaminophen.

Example prediction equations based on simple or multi-variate regressionare provided below. Within these equations, the primary current islabelled as i10. Primary current is the current responsive to theconstant voltage potential applied to the working electrode. The primarycurrent is typically measured prior to application of any probingpotential modulations, for example. That is, in some embodiments,probing potential modulations are applied to the working electrode afterthe primary current has been measured. Any number of probing potentialmodulation steps may be applied (e.g., 1, 2, 3, 4, 5, 6, etc.).Application of a probing potential modulation step causes a non-linearresponse in working electrode current, which may be measured at multipletimes as the response current varies (e.g., 2, 3, 4 or more times) asdescribed previously with reference to FIGS. 7B-7E. Within theprediction equations provided below, probing potential modulationcurrents are labeled as ixy, where x denotes the voltage step and ydenotes at what location (e.g., time) within the voltage step thecurrent is measured. For example, i11 is the first current of the threecurrents recorded during the first voltage step, while i13 is the thirdcurrent of the three currents recorded during the first voltage step.Similarly, i63 is the third current of the three currents recordedduring the sixth voltage step. As mentioned, i10 is the primary currentor current measured at time 0 before probing potential modulations areapplied.

For non-probing potential modulation (nppm) data in which only theprimary data point currents are used, the nppm prediction equationsG_ref_nppm is based on simple regression in equation (3) below. That is,there is only one signal i10 available for expressing the glucose. Evenif additional data points were measured in the time period betweenprimary data points (the time when ppm data is measured), because only aconstant voltage of 0.55 V is employed, the currents that follow i10would still contain the same information as i10.

G_ref_nppm (mg/dL)=−43.147657+6.14967*i10  (3)

For probing potential modulation (ppm) data in which both primary datapoint currents and probing potential modulation currents are used, thecurrents that are generated at different potential modulation steps aredifferent than the generally constant output currents from the constantoperating voltage used for measuring primary data points. These ppmcurrents are correlated with i10 in different ways. As an example ofthese correlations, two graphs of the initial and ending potentialmodulation current correlations are shown in FIGS. 11G and 11H for anacetaminophen background level of 0.2 mg/dL, as provided herein. Thesesubtle relationships are built into the prediction equation G_ref_ppmfor glucose in equation (4) by multi-variate regression:

$\begin{matrix}{{{G\_ ref}{\_ ppm}\mspace{14mu} \left( {{mg}/{dL}} \right)} = {39.07108 - {11.663917*\underset{\_}{i\; 11}} + {18.212602*\underset{\_}{i\; 13}} - {9.318668*\underset{\_}{i\; 21}} + {13.896986*\underset{\_}{i\; 22}} - {9.519628*\underset{\_}{i\; 23}} - {1.947934*\underset{\_}{i\; 31}} + {13.389696*\underset{\_}{i\; 32}} - {12.395404*\underset{\_}{i\; 33}} - {2.851515*\underset{\_}{i\; 41}} + {9.183032*\underset{\_}{i\; 42}} - {2.944314*\underset{\_}{i\; 51}}}} & (4)\end{matrix}$

Note that equations (3) and (4) are merely examples. Other predictionequations may be used.

In another example, three CGM sensors were subjected to a long-term (17day) stability monitoring with probing potential modulations appliedevery 3-minutes periodically, along with a CGM sensor in the samemonitoring employed without the probing potential modulations. Threelinearity runs were carried out at time 0 (immediately after the startof the 17-day monitoring), day-7 and day-14. At times other than thelinearity runs, the CGM sensors were exposed to a constant glucosesolution of 450 mg/dL. The raw current profiles of the primary datapoints for the four sensors are shown in FIG. 12A, which illustrates agraph of working electrode current versus elapsed time for three sensorssubjected to probing potential modulations (sensor ppm-1, ppm-2 andppm-3) and one sensor subjected to no probing potential modulations(sensor nppm-1). It can be seen that the three sensors employing probingpotential modulations (ppm) and the sensor with no probing potentialmodulations (nppm) track with each other temporarily as workingelectrode current moves up and down. The relative sensitivities aremaintained through the entire monitoring of 17 days. This plot of ppmcurrents and nppm currents shows that there is no long-term negativeeffect of probing potential modulation on the primary currents (thecurrents resulting from the constant operating voltage used to generateprimary data points).

There are at least four factors which may contribute to the error indetermined glucose concentrations, or affect the accuracy: (1) theinitial sensor current decay, or the warmup time which limits theability of the sensor system to report accurate analyte concentration inearly stage; (2) the individual sensor sensitivities among differentsensors, or different lots of sensors; (3) the sensitivity changes ofthe sensors over the monitoring time, and (4) background signal changesdue to intakes of interfering substances, such as the medication ofacetaminophen.

The three sensors that employed probing potential modulation haddifferent sensitivities with three different sets of calibrationconstants (slopes and intercepts) in order to determine glucose valuesaccurately if only the primary data information was available. The threesensors sensitivities (slope_1=0.107, Slope_2=0.1532, Slope_3=0.1317)differed by as much as 50% from low to high, which represent significantsensitivity variations. For example, FIG. 12B illustrates workingelectrode current versus glucose concentration for the three sensors(ppm-1, ppm-2, ppm-3) at day-7 in accordance with embodiments providedherein. The conventional method of factory calibration links thesensitivities at release testing to sensor performance, such as by lotconstants, for glucose calculations. If there is any sensitivity change,such as current signals moving up and down, the factory assignedcalibration constants (slope and intercept) will lead to error in thedetermined glucose concentrations.

The initial decay of the sensor currents is a natural tendency of CGMsensors that prevents glucose readings from being reported until a latertime, for instance, 1, 2 or 3 or more hours after sensor insertion (see,FIG. 2, for example). This initial quiescent time is referred to the CGMsensor's warmup time. If this warmup time can be reduced, the CGM systemmay provide glucose readings at a reasonably short time, such as 30minutes, or even 15, 10, or 5 minutes following insertion.

Sensitivity changes over monitoring time may be seen in FIG. 12C, forexample, which illustrates working electrode current versus glucoseconcentration for one of the sensors (sensor ppm-1) at day-1, day-7 andday-14 in accordance with embodiments provided herein. As shown in FIG.12C, the sensitivity of the sensor changes (e.g., decreases) over time.

Sensor current is also dependent on temperature, as shown in FIG. 12Dwhich illustrates working electrode (primary current) of the threesensors with temperature variations during a portion of day-9 oflong-term monitoring.

Because probing potential modulation (ppm) currents contain sensorinformation, the issues of sensitivity differences, initial warmup timeand sensitivity changes during monitoring may be overcome to providemore accurately determined analyte concentrations. For example, apredictive glucose equation may be derived with input parameters such asprobing potential modulation currents and primary data point currents,using for example, multi-variate regression. (While the example belowemploys voltage potential steps, it will be understood that other typesof probing potential modulations may be similarly employed.) For theprovided example of FIG. 12A, the input parameters may be the followingtypes (which are defined below): (1) primary data point current i10 andprobing potential modulation currents i11 to i63, (2) temperature crossterms of the primary data point current i10T and probing potentialmodulation (ppm) currents i11T to i63T, (3) probing potential modulation(ppm) current ratios R1, R2, R3, R4, R5 and R6 within each potentialstep for the six steps in the probing potential modulation sequence, (4)x-type parameters, (5) y-type parameters, (6) z-type parameters, and/or(7) the cross terms of the additional parameters. These terms aredefined as followed:

Probing currents: The probing potential modulation currents i11, i12,i13, . . . , i61, i62, i63, wherein the first digit (x) of the ixyformat denotes the potential step while the second digit (y) denoteswhich current measurement made after application of the potential step(e.g., the first, second or third measurement).

R parameters: These ratios are computed by the ending ppm current beingdivided by the first ppm current within one potential step. For example,R1=i13/i11, R2=i23/i21, R3=i33/i31, R4=i43/i41, R5=i53/i51, andR6=i63/i61.

X-type parameters: The general format for this type of parameter isgiven by the ending ppm current of a later potential step being dividedby the ending ppm current of an earlier potential step. For example,parameter x61 is determined by i63/i13 where i63 is the ending ppmcurrent of step 6 in the three recorded currents per step while i13 isthe ending ppm current of step 1. Additionally, x61=i63/i13,x62=i63/i23, x63=i63/i33, x64=i63/i43, x65=i63/i53, x51=i53/i13,x52=i53/i23, x53=i53/i33, x54=i53/i43, x41=i43/i13, x42=i43/i23,x43=i43/i33, x31=i33/i13, x32=i33/i23, and x21=i23/i13.

Y-type parameters: The general format for this type of parameter isgiven by the ending ppm current of a later potential step being dividedby the first ppm current of an earlier potential step. For example,parameter y61 is determined by i63/i11 where i63 is the ending ppmcurrent of step 6 in the three recorded currents per step while i11 isthe first ppm current of step 1. Additionally, y61=i63/i11, y62=i63/i21,y63=i63/i31, y64=i63/i41, y65=i63/i51, y51=i53/i11, y52=i53/i21,y53=i53/i31, y54=i53/i41, y41=i43/i11, y42=i43/i21, y43=i43/i31,y31=i33/i11, y32=i33/i21, and y21=i23/i11,

Z-type parameters: The general format for this type of parameter isgiven by the first ppm current of a later potential step being dividedby the ending ppm current of an earlier potential step. For example,parameter z61 is determined by i61/i13 where i61 is the first ppmcurrent of step 6 in the three recorded currents per step while i13 isthe ending ppm current of step 1. Additionally, z61=i61/i13,z62=i61/i23, z63=i61/i33, z64=i61/i43, z65=i61/i53, z51=i51/i13,z52=i51/i23, z53=i51/i33, z54=i51/i43, z41=i41/i13, z42=i41/i23,z43=i41/i33, z31=i31/i13, z32=i31/i23, and z21=i21/i13.

Temperature cross terms: Temperature cross terms are computed bymultiplying other parameters by the temperature at which the underlyingcurrents were measured. For example, R1T=(i13/i11)*T, y61T=(i63/i11)*T,etc.

Other types of parameters, such as the ppm current differences orrelative differences carrying the equivalent or similar information, orthe ratios of middle ppm currents, may also be used.

For demonstrating the feasibility of overcoming the issues of differentsensor sensitivities, initial warmup time, sensitivity changes over thelong-term, and different background signals due to the intake ofdifferent amounts of interfering substances, the above parameters, alongwith their temperature cross terms, are employed as the inputs inmulti-variate regression in its simple form. Additional terms/parametersmay be provided in the regression analysis.

Equation 5 below shows the regression equation for predicting glucosewith the ppm data from the three sensors (ppm-1, ppm-2, ppm-3) in the17-day long-term monitoring with three linearity runs of FIGS. 12A-D. Inaddition to the subtle relationships between individual ppm currentsshown in FIGS. 11G and 11H, the different ratio parameters definedpreviously may also be selected and built into the prediction equation.The selected parameters in the resulting equation from thismulti-variate regression are the probing currents and the relatedparameters, also referred to as “self-sufficient information”parameters, which are otherwise not available when only a constantvoltage potential is used. The parameters and/or coefficients inEquation 5 are merely examples. Other number and/or types of parametersand/or coefficients may be used.

$\begin{matrix}{{{G\_ ref}\mspace{14mu} \left( {{mg}/{dL}} \right)} = {{- 3428.448} + {27.64708*\underset{\_}{i\; 10}} - {19.990456*\underset{\_}{i\; 11}} + {5.820128*\underset{\_}{i\; 13}} - {1.933492*\underset{\_}{i\; 21}} + {5.18382*\underset{\_}{i\; 31}} - {5.131074*\underset{\_}{i\; 32}} - {3.451613*\underset{\_}{i\; 33}} + {5.493953*\underset{\_}{i\; 41}} + {23.541526*\underset{\_}{i\; 43}} + {{.41852}*\underset{\_}{i\; 51}} - {1.125275*\underset{\_}{i\; 10T}} + {{.673867}*\underset{\_}{i\; 11T}} + {{.196962}*\underset{\_}{i\; 21T}} - {{.202042}*\underset{\_}{i\; 31T}} + {{.271105}*\underset{\_}{i\; 32T}} - {{.102746}*\underset{\_}{i\; 41T}} - {0.047134*\underset{\_}{i\; 42T}} - {{.889602}*\underset{\_}{i\; 43T}} + {{.06569}*\underset{\_}{i\; 52T}} - {{.374561}*\underset{\_}{i\; 63T}} + {2158.6*\underset{\_}{R\; 1}} + {5210.274*\underset{\_}{R\; 3}} + {3880.969*\underset{\_}{x\; 62}} + {195.9686*\underset{\_}{x\; 51}} + {2939.115*\underset{\_}{x\; 53}} + {500.49*\underset{\_}{x\; 54}} + {2519.018*\underset{\_}{x\; 42}} - {2445.111*\underset{\_}{z\; 53}} - {320.966*\underset{\_}{z\; 41}} + {9593.727*\underset{\_}{y\; 64}} + {4002.55*\underset{\_}{y\; 53}} - {2736.6*\underset{\_}{y\; 54}} - {11649.06*\underset{\_}{y\; 41}} - {43811.72*\underset{\_}{y\; 43}} + {978*\underset{\_}{y\; 31}} - {66.3105*\underset{\_}{R\; 4T}} + {62.13563*\underset{\_}{x\; 61T}} - {170.8194*\underset{\_}{x\; 62T}} + {19.64226*\underset{\_}{x\; 52T}} - {75.8533*\underset{\_}{x\; 53T}} - {61.46921*\underset{\_}{x\; 42T}} + {10.023384*\underset{\_}{z\; 52T}} + {72.26341*\underset{\_}{z\; 53T}} - {5.7766*\underset{\_}{z\; 54T}} - {4.099989*\; \underset{\_}{z\; 41T}} + {16.754378*\underset{\_}{z\; 32T}} - {18.354153*\underset{\_}{z\; 21T}} + {309.6468*\underset{\_}{y\; 61T}} - {1538.808*\underset{\_}{y\; 65T}} + {83.124865*\underset{\_}{y\; 51T}}}} & (5)\end{matrix}$

The results of the regression after applying Equation 5 to the data ofFIGS. 12A-D may be further demonstrated with improved glucose accuracyin FIG. 13A-13F. Corresponding to the current profiles of the threesensors ppm-1, ppm-2 and ppm-3 of FIG. 12A (each having a differentsensitivity), FIG. 13A shows the output glucose values over the 17 dayswhere the differences in glucose values are reduced and the overallglucose accuracy increased with probing potential modulation signalsfeeding the predictive equation for glucose determination (Equation 5).Additionally, the wrinkles in the current profiles due to sensitivitychanges and temperature effects over the 17-day monitoring are alsosmoothed out. Comparing FIGS. 12B and 13B, FIG. 13B shows the threeglucose response lines for sensors ppm-1, ppm-2 and ppm-3. The threesensors with widely different sensitivities produce glucose output linesvirtually overlapping with each other, which further shows the levelingof the sensitivity differences among the three sensors. If sensorsppm-1, ppm-2 and ppm-3 represent three different release sensor lotsfrom manufacturing, with sensitivities ranging ±25% from the center,then the methods provided herein of using the ppm currents forcompensation demonstrate that these methods may accommodate thedifferent sensor sensitivities, and produce high accuracy CGM glucosedeterminations, without having to relying on factory or in-situcalibration.

Additionally, comparing FIGS. 12C and 13C, unlike the initial non-linearbehavior due to the slow warmup time in the order of 30-40 minutes ofFIG. 12C, the output glucose values shown in FIG. 13C at the initialglucose of 50 mg/dL show no non-linear characteristics as these areremoved due to the regression, thus making the initial startup time forproviding accurate glucose readings as early as 5-10 minutes. Thetemperature effect shown in FIG. 12D is shown to have been removed inFIG. 13D and with the virtually overlapped glucose profiles from thethree sensors ppm-1, ppm-2 and ppm-3. The effect of leveling thesensitivity differences and shortening the warmup time can be furtherseen in FIG. 13E (raw currents at the beginning of monitoring) and FIG.13F (the glucose calculated by the prediction equation, Equation 5, withinputs from the ppm currents). A steady-state glucose concentrationprofile is produced with about 1 hour and the glucose profile of thethree sensors align immediately.

In summary, employing probing potential modulations (ppms) as describedherein provides enough self-sufficient information to accommodatesensitivity differences among different sensor lots, sensitivity changeover an entire continuous monitoring time period, background variationsdue to different levels of interference species, and non-linear effectof glucose signals immediately after insertion and activation (providinga shortened warmup time). This may be accomplished with ppm currents andwithout the factory and/or in-situ calibrations.

FIG. 14A illustrates a high-level block diagram of an example CGM device1400 in accordance with embodiments provided herein. Although not shownin FIG. 14A, it is to be understood that the various electroniccomponents and/or circuits are configured to couple to a power supply,such as but not limited to a battery. CGM device 1400 includes a biascircuit 1402 that may be configured to couple to a CGM sensor 1404. Biascircuit 1402 may be configured to apply a bias voltage, such as acontinuous DC bias, to an analyte-containing fluid through CGM sensor1404. In this example embodiment, the analyte-containing fluid may behuman interstitial fluid, and the bias voltage may be applied to one ormore electrodes 1405 of CGM sensor 1404 (e.g., a working electrode, abackground electrode, etc.).

Bias circuit 1402 also may be configured to apply a probing potentialmodulation sequence, as shown in FIGS. 7B-7E or another probingpotential modulation sequence, to CGM sensor 1404. For example, probingpotential modulation sequences may be applied initially and/or atintermediate time periods as described above with reference to FIGS.1-6, or applied for each primary data point as described above withreference to FIGS. 7A-13F. Probing potential modulation sequences may beapplied before, after, or before and after measurement of a primary datapoint, for example.

In some embodiments, the CGM sensor 1404 may include two electrodes andthe bias voltage and probing potential modulations may be applied acrossthe pair of electrodes. In such cases, current may be measured throughthe CGM sensor 1404. In other embodiments, the CGM sensor 1404 mayinclude three electrodes such as a working electrode, a counterelectrode and a reference electrode. In such cases, the bias voltage andprobing potential modulations may be applied between the workingelectrode and the reference electrode, and current may be measuredthrough the working electrode, for example. The CGM sensor 1404 includeschemicals which react with a glucose-containing solution in areduction-oxidation reaction, which affects the concentration of chargecarriers and the time-dependent impedance of the CGM sensor 1404.Example chemicals include glucose oxidase, glucose dehydrogenase, or thelike. In some embodiments, a mediator such as ferricyanide or ferrocenemay be employed.

The continuous bias voltage generated and/or applied by bias circuit1402 may range from about 0.1 to 1 volts versus the reference electrode,for example. Other bias voltages may be used. Example probing potentialmodulations values are described previously.

Probing potential modulation (ppm) currents and non-probing potentialmodulation (nppm) currents through CGM sensor 1404 in ananalyte-containing fluid responsive to probing potential modulations anda constant bias voltage may be conveyed from CGM sensor 1404 to acurrent measurement (T_(meas)) circuit 1406 (also referred to as currentsensing circuitry). Current measurement circuit 1406 may be configuredto sense and/or record current measurement signals that have magnitudesindicative of the magnitudes of the currents conveyed from CGM sensor1404 (e.g., using a suitable current-to-voltage converter (CVC), forexample). In some embodiments, current measurement circuit 1406 mayinclude a resistor having a known nominal value and a known nominalprecision (e.g., 0.1% to 5%, or even smaller than 0.1%, in someembodiments), through which the current conveyed from CGM sensor 1404 ispassed. A voltage developed across the resistor of current measurementcircuit 106 represents the magnitude of the current, and may be referredto as the current measurement signal (or raw glucose signalSignal_(Raw)).

In some embodiments, a sample circuit 1408 may be coupled to currentmeasurement circuit 1406, and may be configured to sample the currentmeasurement signal, and may produce digitized time-domain sample datathat is representative of the current measurement signal (e.g.,digitized glucose signals). For example, sample circuit 1408 may be anysuitable A/D converter circuit configured to receive the currentmeasurement signal, which is an analog signal, and convert it to adigital signal having a desired number of bits as an output. The numberof bits output by sample circuit 1408 may be sixteen in someembodiments, but more or fewer bits may be used in other embodiments. Insome embodiments, sample circuit 1408 may sample the current measurementsignal at a sampling rate in the range of about 10 samples per second to1000 samples per second. Faster or slower sampling rates may be used.For example, sampling rates such as about 10 kHz to 100 kHz may be usedand down-sampled to further reduce signal-to-noise ratio. Any suitablesampling circuitry may be employed.

Still referring to FIG. 14A, a processor 1410 may be coupled to samplecircuit 1408, and may be further coupled to a memory 1412. In someembodiments, processor 1410 and sample circuit 1408 are configured todirectly communicate with each other via a wired pathway (e.g., via aserial or parallel connection). In other embodiments, the coupling ofprocessor 1410 and sample circuit 1408 may be by way of memory 1412. Inthis arrangement, sample circuit 1408 writes digital data to memory1412, and processor 1410 reads the digital data from memory 1412.

Memory 1412 may have stored therein one or more prediction equations1414 (e.g., Equation 5) for use in determining glucose values based onprimary data points (nppm currents) and probing potential modulation(ppm) currents (from current measurement circuit 1406 and/or samplecircuit 1408). For example, in some embodiments, two or more predictionequations may be stored in memory 1412, each for use with differentsegments (time periods) of CGM collected data. In some embodiments,memory 1412 may include a prediction equation based on primary currentsignals generated by application of a constant voltage potential appliedto a reference sensor (e.g., ppm-1, ppm-2 and/or ppm-3 of FIGS. 12A, forexample), and a plurality of probing potential modulation currentsignals generated by application of a probing potential modulationsequence applied between primary current signal measurements.

Additionally or alternatively, memory 1412 may have stored there incalibration indices computed based on potential probing modulationcurrents for use during in-situ calibrations as described previously.

Memory 1412 also may have stored therein a plurality of instructions. Invarious embodiments, processor 1410 may be a computational resource suchas but not limited to a microprocessor, a microcontroller, an embeddedmicrocontroller, a digital signal processor (DSP), a field programmablegate array (FPGA) configured to perform as a microcontroller, or thelike.

In some embodiments, the plurality of instructions stored in memory 1412may include instructions that, when executed by the processor 1410,cause the processor 1410 to (a) cause the CGM device 1400 (via biascircuit 1402, CGM sensor 1404, current measurement circuit 1406 and/orsample circuit 1408) to measure current signals (e.g., primary currentsignals and probing potential modulation current signals) frominterstitial fluid; (b) store current signals in memory 1412; (c)compute calibration indices and/or prediction equation parameters suchas ratios (and/or other relationships) of currents from differentpulses, voltage steps or other voltage changes within a probingpotential modulation sequence; (d) employ computed prediction equationparameters to compute glucose values (e.g., concentrations) usingprediction equations; (e) compute calibration indices; (e) communicateglucose values to a user; and/or (f) conduct in-situ calibrations basedon computed calibration indices.

Memory 1412 may be any suitable type of memory, such as but not limitedto, one or more of a volatile memory and/or a non-volatile memory.Volatile memory may include, but is not limited to a static randomaccess memory (SRAM), or a dynamic random access memory (DRAM).Non-volatile memory may include, but is not limited to, an electricallyprogrammable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), a flash memory (e.g., a type ofEEPROM in either of the NOR or NAND configurations, and/or in either thestacked or planar arrangements, and/or in either the single-level cell(SLC), multi-level cell (MLC), or combination SLC/MLC arrangements), aresistive memory, a filamentary memory, a metal oxide memory, a phasechange memory (such as a chalcogenide memory), or a magnetic memory.Memory 112 may be packaged as a single chip or as multiple chips, forexample. In some embodiments, memory 112 may be embedded, with one ormore other circuits, in an integrated circuit, such as, for example, anapplication specific integrated circuit (ASIC).

As noted above, memory 1412 may have a plurality of instructions storedtherein that, when executed by processor 1410, cause processor 1410 toperform various actions specified by one or more of the stored pluralityof instructions. Memory 1412 may further have portions reserved for oneor more “scratchpad” storage regions that may be used for read or writeoperations by processor 1410 responsive to execution of one or moreinstructions of the plurality of instructions.

In the embodiment of FIG. 14A, bias circuit 1402, CGM sensor 1404,current measurement circuit 1406, sample circuit 1408, processor 1410,and memory 1412 including prediction equation(s) 1414, may be disposedwithin a wearable sensor portion 1416 of CGM device 1400. In someembodiments, wearable sensor portion 1416 may include a display 1417 fordisplaying information such as glucose concentration information (e.g.,without use of external equipment). Display 1417 may be any suitabletype of human-perceivable display, such as but not limited to, a liquidcrystal display (LCD), a light-emitting diode (LED) display, or anorganic light emitting diode (OLED) display.

Still referring to FIG. 14A, CGM device 1400 may further include aportable user device portion 1418. A processor 1420 and a display 1422may be disposed within portable user device portion 1418. Display 1422may be coupled to processor 1420. Processor 1420 may control the text orimages shown by display 1422. Wearable sensor portion 1416, and portableuser device portion 1418, may be communicatively coupled. In someembodiments the communicative coupling of wearable sensor portion 1416,and portable user device portion 1418, may be by way of wirelesscommunication via transmitter circuitry and/or receiver circuitry, suchas transmit/receive circuit TxRx 1424 a in wearable sensor portion 1416and transmit/receive circuit TxRx 1424 b in portable user device 1418,for example. Such wireless communication may be by any suitable meansincluding but not limited to standards-based communications protocolssuch as the Bluetooth® communications protocol. In various embodiments,wireless communication between wearable sensor portion 1416, andportable user device portion 1418, may alternatively be by way ofnear-field communication (NFC), radio frequency (RF) communication,infra-red (IR) communication, or optical communication. In someembodiments, wearable sensor portion 1416 and portable user deviceportion 1418 may be connected by one or more wires.

Display 1422 may be any suitable type of human-perceivable display, suchas but not limited to, a liquid crystal display (LCD), a light-emittingdiode (LED) display, or an organic light emitting diode (OLED) display.

Referring now to FIG. 14B, an example CGM device 1450 is shown that issimilar to the embodiment illustrated in FIG. 14A, but having adifferent partitioning of components. In CGM device 1450, the wearablesensor portion 1416 includes the bias circuit 1402 coupled to the CGMsensor 1404, and the current measurement circuit 1406 coupled to the CGMsensor 1404. The portable user device portion 1418 of CGM device 1450includes the sample circuit 1408 coupled to processor 1420, and thedisplay 1422 coupled to processor 1420. Processor 1420 is furthercoupled to memory 1412 that may include prediction equation(s) 1414stored therein. In some embodiments, processor 1420 in CGM device 1450may also perform the previously-described functions performed byprocessor 1410 of CGM device 1400 of FIG. 14A, for example. Wearablesensor portion 1416 of CGM device 1450 may be smaller and lighter, andtherefore less invasive, than CGM device 1400 of FIG. 14A because samplecircuit 1408, processor 1410, memory 1412, etc., are not includedtherein. Other component configurations may be employed. For example, asa variation to the CGM device 1450 of FIG. 14B, sample circuit 1408 mayremain on wearable sensor portion 1416 (such that portable user device1418 receive digitize glucose signals from wearable sensor portion1416).

FIG. 15 is a side schematic view of an example glucose sensor 1404 inaccordance with embodiments provided herein. In some embodiments,glucose sensor 1404 may include a working electrode 1502, a referenceelectrode 1504, a counter electrode 1506 and a background electrode1508. The working electrode may include a conductive layer coated with achemical which reacts with a glucose-containing solution in areduction-oxidation reaction (which affects the concentration of chargecarriers and the time-dependent impedance of the CGM sensor 1404). Insome embodiments, the working electrode may be formed from platinum orsurface roughened platinum. Other working electrode materials may beused. Example chemical catalysts (e.g., enzymes) for the workingelectrode 1502 include glucose oxidase, glucose dehydrogenase, or thelike. The enzyme component may be immobilized onto the electrode surfaceby a cross-linking agent such as glutaraldehyde, for example. An outermembrane layer may be applied onto the enzyme layer to protect theoverall inner components including the electrode and the enzyme layer.In some embodiments, a mediator such as ferricyanide or ferrocene may beemployed. Other chemical catalysts and/or mediators may be employed.

In some embodiments, reference electrode 1504 may be formed fromAg/AgCl. The counter electrode 1506 and/or the background electrode 1508may be formed a suitable conductor such as platinum, gold, palladium, orthe like. Other materials may be used for the reference, counter and/orbackground electrodes. In some embodiments, the background electrode1508 may be identical to the working electrode 1502, but without thechemical catalyst and mediator. Counter electrode 1506 may be isolatedfrom the other electrodes by an isolation layer 1510 (e.g., polyimide oranother suitable material).

While described primarily with regarding to glucose concentrationdeterminations during continuous glucose monitoring, it will beunderstood that embodiments described herein may be used with othercontinuous analyte monitoring systems (e.g., cholesterol, lactate, uricacid, alcohol, or other analyte monitoring systems). For example, one ormore prediction equations similar to Equation 5 may be developed for anyanalyte to be monitored through use of probing potential modulationoutput currents and their related cross terms. Similarly, probingpotential modulation output currents may be measured for other analytesand used to compute calibration indices for use during in-situcalibrations.

FIG. 16 is a flowchart of an example method 1600 of compensating forerrors during continuous glucose monitoring (CGM) measurements inaccordance with embodiments provided herein. Method 1600 includesproviding a CGM device including a sensor, a memory and a processor(Block 1602), such as wearable sensor portion 1416 of FIG. 14A, forexample. Method 1600 also includes applying a constant voltage potentialto the sensor (Block 1604), measuring primary current signals resultingfrom the constant voltage potential (Block 1606), and storing measuredprimary current signals in the memory (Block 1608). As described withreference to FIG. 7A, a constant voltage potential may be applied to aworking electrode of an analyte sensor. In response to the constantvoltage potential, primary current signals may be generated by thesensor, measured and stored in a memory (e.g., memory 412).

Between measurements of primary current signals, method 1600 includesapplying a probing potential modulation sequence to the sensor (Block1610), measuring probing potential modulation current signals resultingfrom the probing potential modulation sequence (Block 1612) and storingmeasured probing potential modulation current signals in the memory(Block 1614). For example, FIGS. 7B-7F illustrate example probingpotential modulation sequences that may be applied between primarycurrent signal measurements, resulting in probing potential modulationcurrents that may be measured and stored in memory. For each primarycurrent signal, method 1600 may include employing the primary currentsignal and a plurality of the measured probing potential modulationcurrent signals associated with the primary current signal to determinea glucose value (Block 1616). In some embodiments, a prediction equationsimilar to equation 5 may be used to compute glucose values based onprimary current signals and probing potential modulation currentsmeasured after (and/or before) each primary current signal, aspreviously described. The probing potential modulation currents usedwith a primary current signal to determine a glucose or other analytevalue may be referred to as being “associated with” the primary currentsignal. For example, probing potential modulation currents measuredbefore or after a primary current signal is measured may be associatedwith the primary current signal (if used to compute a glucose or otheranalyte value).

FIG. 17 is a flowchart of an example method 1700 of making a continuousglucose monitoring (CGM) device in accordance with embodiments providedherein. Method 1700 includes creating a prediction equation based on aplurality of probing potential modulation current signals measured for areference CGM sensor in response to a probing potential modulationsequence applied to the reference CGM sensor before or after primarycurrent signals are measured for the reference CGM sensor (Block 1702).For example, a reference CGM sensor may include one or more CGM sensorsused to generate primary data points and ppm currents in response toreference glucose concentrations represented by BGM readings (e.g.,primary current and ppm currents measured for the purpose of determiningprediction equations that are subsequently stored in a CGM device andused during continuous glucose monitoring).

Method 1700 also includes providing a CGM device including a sensor, amemory and a processor (Block 1704); storing the prediction equation inthe memory of the CGM device (1706); and storing computer program code(Block 1708) in the memory of the CGM device that, when executed by theprocessor, causes the CGM device to (a) apply a constant voltagepotential to the sensor, measure primary current signals resulting fromthe constant voltage potential and store measured primary currentsignals in the memory; (b) between measurements of primary currentsignals, apply a probing potential modulation sequence to the sensor,measure probing potential modulation current signals resulting from theprobing potential modulation sequence and store measured probingpotential modulation current signals in the memory; (c) for each primarycurrent signal, employ the primary current signal, a plurality of themeasured probing potential modulation current signals associated withthe primary current signal and the stored prediction equation todetermine a glucose value; and (d) communicate determined glucose valuesto a user of the CGM device.

FIG. 18 is a flowchart of an example method 1800 of determining analyteconcentrations during continuous monitoring measurements in accordancewith embodiments provided herein. Method 1800 includes inserting abiosensor subcutaneously into a subject, the biosensor including acounter electrode, a reference electrode and a working electrode havinga chemical composition configured to oxidize an analyte (Block 1802);applying a constant voltage to the working electrode having the chemicalcomposition so as to generate a continuous current flow from the workingelectrode (1804); sensing and storing primary current signals from theworking electrode into a memory (1806); after sensing each primarycurrent signal, applying a probing potential modulation sequence to theworking electrode, and sensing and storing probing potential modulationcurrents generated in response to the probing potential modulationsequence into the memory (1808); gathering a primary current signal andprobing potential modulation currents generated after the primarycurrent signal (1810); and employing the gathered primary current signaland probing potential modulation currents to compute an analyte value(1812).

FIG. 19 is a flowchart of an example method 1900 of probing a conditionof a continuous analyte monitoring (CAM) sensor and of calibrating thesensor based thereon in accordance with embodiments provided herein.Method 1900 includes applying an operating voltage to the CAM sensor(Block 1902); probing a condition of the CAM sensor by applying at leastone voltage potential step greater than the operating voltage and atleast one voltage potential step less than the operating voltage (Block1904); measuring output currents of the CAM sensor in response to theprobing (Block 1906); calculating calibration indices via ratios of theoutput currents (Block 1908); and calibrating the CAM sensor based onthe calibration indices (Block 1910).

FIG. 20 is a flowchart of an example method 2000 of applying probingpotential modulation during continuous analyte monitoring fordetermination of analyte concentration in accordance with embodimentsprovided herein. Method 2000 includes applying a constant operatingvoltage to an analyte sensor during a continuous sensor operation (Block2002). For example, a constant voltage potential, such as 0.55 Volts oranother suitable value, may be applied to the working electrode of asensor. Block 2004 includes applying at least one probing potentialmodulation step different than the constant operating voltage in eachcycle of the continuous sensor operation. As shown in FIGS. 7B-7F, aprobing potential modulation sequence of voltage steps may be applied tothe working electrode of a sensor so as to generate probing potentialmodulations currents, for example. Block 2006 includes measuring primarycurrent from the constant operating voltage in each cycle and at leastone companion probing potential modulation current in each cycle,responsive to analyte concentration. For example, following eachmeasurement of a primary data point (e.g., a current signal caused bythe constant operating voltage), probing potential modulation currentsmay be generated by application of a probing potential modulationsequence and these probing potential modulation currents may be measured(as “companion” probing potential modulation currents to the primarydata point). Block 2008 includes determining analyte concentration fromthe primary current and the at least one companion probing potentialmodulation current from the at least one probing potential modulationstep.

In some embodiments, determining analyte concentration may includeaccommodating a sensor sensitivity variation of at least ±25% from acenter sensitivity of a manufacturing release without factorycalibration. That is, large sensitivity variations between sensors maybe accommodated through the use of ppm currents, without the use offactory calibrations. Further, in some embodiments, determining analyteconcentration may include accommodating effects of background signalchanges by at least 5 times. For example, analyte concentrations may beaccurately determined by using ppm currents despite a 5 times change inbackground interference signals as described previously with referenceto FIGS. 11A-11F.

In some embodiments, determining analyte concentration may includeaccommodating daily sensitivity changes without an in-situ calibration.For example, analyte concentrations may be accurately determined byusing ppm currents despite daily sensitivity changes as described withreference to FIGS. 13A-13D (without using in-situ calibrations).Likewise, in some embodiments, determining analyte concentration mayinclude determining analyte concentration with a warmup time not longerthan 30 minutes as previously described (see, for example, FIG. 12C).

As mentioned, while described primarily with regarding to glucoseconcentration determinations during continuous glucose monitoring, itwill be understood that embodiments described herein may be used withother continuous analyte monitoring systems (e.g., cholesterol, lactate,uric acid, alcohol, or other analyte monitoring systems). For example,in some embodiments, a continuous analyte monitoring (CAM) device may beprovided that includes a wearable portion having a sensor configured tobe subcutaneously inserted into a subject and to produce current signalsfrom interstitial fluid (e.g., wearable sensor portion 1416), aprocessor (e.g., processor 1410) and a memory (e.g., memory 412) coupledto the processor. The memory may include computer program code storedtherein that, when executed by the processor, causes the CAM device to:(a) apply a constant voltage to the sensor so as to generate a primarycurrent flow from the sensor; (b) sense and store primary currentsignals generated in response to the constant voltage into the memory;(c) between sensing primary current signals, apply a probing potentialmodulation sequence to the sensor, and sense and store probing potentialmodulation currents generated in response to the probing potentialmodulation sequence into the memory; and (d) employ primary currentsignals and probing potential modulation currents to compute analytevalues over a time period of at least a week (e.g., 7-14 days). In someembodiments, through use of probing potential modulation currents, theCAM device need not employ an in-situ calibration at any point duringcontinuous analyte monitoring (e.g., no finger sticks or in-situcalibration for 7 to 14 days), such as to accommodate for sensorsensitivity changes or background signal changes due to different levelsof interference substances. In some embodiments, through use of probingpotential modulation currents, the CAM device may have a warm up time ofnot more than 30 minutes, and in some cases 5-15 minutes or less.Likewise, in some embodiments, use of probing potential modulationcurrents may eliminate the need for the CAM device to be factorycalibrated (e.g., to accommodate lot-to-lot variations).

The foregoing description discloses example embodiments of thedisclosure. Modifications of the above-disclosed apparatus and methodswhich fall within the scope of the disclosure should be readily apparentto those of ordinary skill in the art. Accordingly, while the presentdisclosure has been disclosed in connection with example embodiments, itshould be understood that other embodiments may fall within the scope ofthe disclosure, as defined by the following claims.

The invention claimed is:
 1. A method of probing a condition of acontinuous analyte monitoring (CAM) sensor and of calibrating the sensorbased thereon, the method comprising: applying an operating voltage tothe CAM sensor; probing a condition of the CAM sensor by applying atleast one voltage potential step greater than the operating voltage andat least one voltage potential step less than the operating voltage;measuring output currents of the CAM sensor in response to the probing;calculating calibration indices via ratios of the output currents; andcalibrating the CAM sensor based on the calibration indices.
 2. Themethod of claim 1 wherein the operating voltage is E₀, the at least onevoltage potential step greater than the operating voltage is E₁ andwherein E₁-E₀ is between 0.05 and 0.3 volts.
 3. The method of claim 1wherein the operating voltage is E₀, the at least one voltage potentialstep less than the operating voltage is E₂ and wherein E₂-E₀ is between−0.05 and −0.5 volts.
 4. The method of claim 1 wherein the at least onevoltage potential step less than the operating voltage is selected toset a mediator of the CAM sensor in a partial reduced state.
 5. Themethod of claim 1 wherein calculating calibration indices via ratios ofthe output currents comprises determining a ratio of a first outputcurrent produced by a voltage potential step greater than the operatingvoltage to a second output current produced by a voltage potential stepless than operating voltage.
 6. The method of claim 5 wherein the firstoutput current is produced at an end of the voltage potential stepgreater than the operating voltage and the second output current isproduced at an end of the voltage potential step less than the operatingvoltage.
 7. The method of claim 1 further comprising applying a secondvoltage potential step greater than or equal to the operating voltageafter the at least one voltage potential step less than the operatingvoltage and applying a second voltage potential step less than thenoperating voltage after the second voltage potential step greater thanor equal to the operating voltage.
 8. The method of claim 1 furthercomprising storing calibration indices in a memory of a CAM deviceemploying the CAM sensor for use during one or more subsequent in-situcalibrations.
 9. The method of claim 1 wherein the CAM sensor is acontinuous glucose monitoring sensor.
 10. The method of claim 1 furthercomprising probing a condition of the CAM sensor every 1-15 minutes. 11.Continuous analyte monitoring (CAM) sensor apparatus, comprising: amanagement unit including a wireless transmitter/receiver incommunication with a wireless transmitter coupled to an on-body sensor,the management unit further comprising a processor, a memory, andsoftware, wherein the processor and software are operative to: apply anoperating voltage to the on-body sensor; probe a condition of theon-body sensor by applying at least one voltage potential step greaterthan the operating voltage and at least one voltage potential step lessthan the operating voltage; measure output currents of the on-bodysensor in response to the probing; calculate calibration indices viaratios of the output currents; and calibrate the on-body sensor based onthe calibration indices.
 12. The CAM sensor apparatus of claim 11wherein the operating voltage is E₀, the at least one voltage potentialstep greater than the operating voltage is E₁ and wherein E₁-E₀ isbetween 0.05 and 0.3 volts.
 13. The CAM sensor apparatus of claim 11wherein the operating voltage is E₀, the at least one voltage potentialstep less than the operating voltage is E₂ and wherein E₂-E₀ is between−0.05 and −0.5 volts.
 14. The CAM sensor apparatus of claim 11 whereinthe at least one voltage potential step less than the operating voltageis selected to set a mediator of the CAM sensor in a partial reducedstate.
 15. The CAM sensor apparatus of claim 11 wherein the processorand software are operative to calculate calibration indices bydetermining a ratio of a first output current produced by a voltagepotential step greater than the operating voltage to a second outputcurrent produced by a voltage potential step less than operatingvoltage.
 16. The CAM sensor apparatus of claim 15 wherein the firstoutput current is produced at an end of the voltage potential stepgreater than the operating voltage and the second output current isproduced at an end of the voltage potential step less than the operatingvoltage.
 17. The CAM sensor apparatus of claim 11 wherein the processorand software are operative to apply a second voltage potential stepgreater than or equal to the operating voltage after the at least onevoltage potential step less than the operating voltage and apply asecond voltage potential step less than then operating voltage after thesecond voltage potential step greater than or equal to the operatingvoltage.
 18. The CAM sensor apparatus of claim 11 wherein the processorand software are operative to store calibration indices in the memory ofthe CAM sensor apparatus for use during one or more subsequent in-situcalibrations.
 19. The CAM sensor apparatus of claim 11 wherein the CAMsensor is a continuous glucose monitoring sensor.
 20. The CAM sensorapparatus of claim 11 wherein the processor and software are operativeto probe a condition of the on-body sensor every 1-15 minutes.